Creating Value from America s Forest Census

Transcription

1 Creating Value from America s Forest Census Richard Guldin Charles H. Perry Barry T. Wilson C. Kenneth Brewer USDA Forest Service, Research & Development Science you can use

2 Bottom Line Up Front Opportunities are emerging to broaden the information available for analyses contextual computing A suite of new information products are emerging from Forest Service Research & Development s FIA Program that offer promise for expanding the types of geospatial analyses that can be conducted The success of these ventures depends on harnessing the opportunities for - Cloud-based computing - Using multiple platforms/mobile devices - Integrating information products from multiple sources

3 What s Contextual Computing? During my professional lifetime, I ve experienced major shifts in technology supporting forestry Mainframe Computing Personal Computing Mobile Computing Contextual Computing??? Computing Contextual computing is analysis in the context of a user s location and situation PUSH not PULL

4 What s Contextual Computing? Mainframe Computing Personal Computing Mobile Computing Contextual Computing??? Computing Here Today? What might contextual computing mean for forestry geospatial applications? Here s what I think contextual computing will mean: Bigger and better electronic team rooms New information products Create new insights into context of how clients & society value & use forests and related natural resources Integrates social with biological/physical to create ecological

5 Benefits of Contextual Computing? Bigger and better electronic team rooms - Empower who can enter the room to use information - Enable others to bring their own information, add information from others, & leave information behind when they log off - New data governance practices. New information products - Composite data layers & displays consolidated federal, state, NGO data - Blended information--photos pinned to data layers Users/visitors to be the eyes of resource managers & other users - Consistent information across devices & systems (e.g., Android, IOS, ArcGIS)

6 Benefits of Contextual Computing Create new insights into context of how clients & society value & use forests and related natural resources - Shared sentiments, expertise, desire, intentions via social media - Better clarity about who, what, when, where, why and how forest values & uses add to quality-of-life. Integrates social information with bio/physical information to create broader ecological information - Geospatial analysis started with biological & physical information; now social information adds value - Now, we are seeing people as part of the ecosystem

7 What does this mean for geospatial analyses? Biological & physical data layers will continue to be important the root of forest geospatial analyses New ways are needed to make sense of social information & transform it into information usable with biological & physical data in geospatial analyses - Much social information is unstructured so ways are needed to analyze it & link it to more structured forest geospatial info - Technologies & analytics needed to deliver & comb through social information in near real time to identify the nuggets useful for geospatial analyses important to land managers

8 Transition to Second Segment A suite of new information products are emerging from Forest Service Research & Development s FIA Program that offer promise for expanding the types of geospatial analyses that can be conducted

9 Forest Inventory & Analysis (FIA) The Nation s forest census; a leading program globally Covers 800 million acres of public and private forest land - Alaska to Puerto Rico, Maine to Guam - Urban to remote wild lands/wilderness Data collection since the 1930s; approaching 80 th anniversary Combines remote sensing information; biological/physical measurements in the field; imputation modeling, estimation, & analyses in the office; surveys of private forest landowner values & preferences; surveys of industrial firms wood usage Reports at multiple spatial scales; state, region, national Broad range of information users: agencies, firms, NGOs, investors; consultants; academics

10 New FIA-Based Information Products Moving from reports to portfolio of information products Shifting from tables of information by categories/clusters to data layers Innovations the past decade, based on FIA information: - Forest Insect & Disease Risk Maps - Forest cover change detection & biomass maps - Monitoring Trends in Burn Severity (MTBS) - LANDFIRE database

11 Insect & Disease Risk Maps Southern Pine Beetle Hazard Map (Red/High to Green/Low)

12 Monitoring Tree Cover Change with Landsat Trend Anomaly

13 Monitoring Trends in Burn Severity MTBS characterizes burn severity on all large fires in CONUS, AK, HI, PR - All ownerships - Historical (since 1984) - Landsat-based, 30m resolution Jointly implemented by FS (Remote Sensing Applications Ctr) and USGS (EROS) Interagency sponsorship by Wildland Fire Leadership Council Landsat NBR Burn Severity Pre-fire dnbr Difference Fire Perimeter Post-fire

14 North American Forest Dynamics Project NASA funded project designed to characterize disturbance patterns and recovery rates of forests across the continent. Goal: Determine the role of forest dynamics in North American carbon balance Uses FIA data for validation and training

15 Forthcoming Information Products We are also hard at work on several new information products that are just nearing completion - A 30-m resolution raster data surface describing forest composition and structure using nearest neighbor imputation from FIA plot data (NN data surface), across all forests of the lower 48 states. - FIAtlas

16 What is a Nearest Neighbor surface? Imputation is a method for filling in missing values in a dataset, from known values in that same dataset. NN imputation mapping is a method for building raster data surfaces from forest inventory data (Eskelson 2009). The family of imputation mapping methods includes: - k-nn (Tomppo 1991), McRoberts et.al. (2002) - MSN (Moeur and Stage 1995) - GNN (Ohmann et. al. 2002) - RFNN (Crookston and Finley 2008) FIA developers & partners have completed CONUS mapping at the 250m scale and are now working at the 30m scale, based on known information from 300,000+ FIA field plots

17 Nearest Neighbor Analysis Examples Feasibility demonstrated - Regional-scale projects (e.g., Pierce et al., 2009, Ohmann et al. 2011). - National pilot project (Nationwide Forest Imputation Study, Grossmann et al., 2009). - Related nationwide projects: - National Land Cover Dataset s Canopy layer update (Coulston et al. 2012) m resolution nationwide NN data surfaces (Wilson et al., 2012). - Landfire s Tree-list layer (Drury and Hernyk 2011) - North American Forest Dynamics project (NAFD, Goward et al., 2008). - Monitoring Trends in Burn Severity (Eidenshink et.al, 2007)

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19 Elements: Introductory context with compelling imagery

20 Elements: Polygon and raster maps at various scales

21 Elements: Dot maps & ancillary graphics

22 Elements: Choropleth maps & novel representations

23 Elements: Proportional symbol maps & regional insets

24 Elements: Image storylines to give the maps a human face

25 Elements: Content from a variety of data providers

26 Elements: Extensive library of atlas layers

27 Elements: Linkages to other data repositories like ArcGIS Online

28 Transition to Third Segment The success of these ventures in contextual computing and forthcoming forest information products depends on harnessing the opportunities for: - Cloud-based computing - Increased mobility - Integrating information products from multiple sources

29 Why the Cloud? Fewer security issues & hassles for non-forest Service information users lower risks Improved accessibility of information work from anywhere; everyone working off the same data layers across organizations & time zones Improved flexibility scalability, pay-as-you-go Openness and transparency of data & information products to a broader user community increased collaboration Lower carbon footprint environmentally friendly - 30+% reduction in energy consumption and carbon usage compared to on-site servers

30 Why Increased Mobility Proliferation of hardware & software platforms in use - Smartphones, tablets, ultrabooks & operating systems When people want info, they want it NOW!! Wherever they are. This user demand for more mobility is changing expectations. - Four bars of cellphone service everywhere - Faster cellular networks and more WiFi hotspots Create an information product/layer/map once; use it many times saves time (money & bandwidth) & energy (intellectual as well as electrical) - Big issue for FIA is so many people downloading the same data to develop the same data layers BEFORE they can really begin analyses to answer their questions - That was the catalyst for FIAtlas!

31 Why Integrated Information from Multiple Sources? Trouble storing partners data layers and analytical applications on FS servers inside the firewall Types of information of value to resource managers and policy makers are increasing; geospatial analyses need to grow - Structured Data --> databases (raw/cleaned, modeled/imputed/classified, metadata - Digital images/photographs - Unstructured text & data from social media - ALL of it with locations geo-referenced with varying precision

32 Why Integrated Information from Multiple Sources? Critical to find ways to integrate all this information from multiple sources including the FIA program and other FS research, development & applications activities Why? - Because it contributes to contextual computing - Because people want to be more connected to each other and the places they visit in ways that are MEANINGFUL TO THEM

33 Bottom Line --AGAIN Opportunities are emerging to broaden the information available for analyses contextual computing is emerging as the new normal A suite of new information products are emerging from Forest Service Research & Development s FIA Program that offer promise for expanding the types of geospatial analyses that can be conducted The success of these ventures depends on harnessing the opportunities for - Cloud-based computing - Using multiple platforms/mobile devices - Integrating information products from multiple sources

34 Closing Thoughts There are just two things on this material earth people and natural resources. Gifford Pinchot (1947) Breaking New Ground (An insight from a February 1907 evening horseback ride in Rock Creek Park, Washington, DC) Forest geospatial activities need to move aggressively into the contextual computing era! - Blending information from people; their attitudes & sentiments from bubbling social media & other unstructured data - With more traditional bio/physical information about natural resource conditions and trends. Why? To create new value & insights for the people who use/ depend/love forests as well as for the people who manage them! - Better information Better dialogue Better decisions The tools exist. Creative & innovative uses are needed.

35 Contact Information: Richard Guldin - rguldin@fs.fed.us Charles H. Perry - charleshperry@fs.fed.us Barry T. Wilson - barrywilson@fs.fed.us C. Kenneth Brewer - ckbrewer01@gmail.com The FIA Program -

36 Technical References Coulston, JW, GG Moisen, BT Wilson, MV Finco, WB Cohen, et al. (2012). Modeling Percent Tree Canopy Cover: A Pilot Study. Photogrammetric engineering and remote sensing 78(7): Crookston, NL and AO Finley (2008). YALMPUTE: An R package for knn imputation. Journal of Statistical Software 23(10): Drury, SA and JM Herynk (2011). The national tree-list layer, US Department of Agriculture, Forest Service, Rocky Mountain Research Station. Eidenshenk, J. Schwind, B., Brewer, K., Zhu, Z., Quayle, B. & Howard, S. (2007). A Project for Monitoring Trends in Burn Severity. Fire Ecology Special Issue. Vol. 3, No. 1. Eskelson, BNI, H Temesgen, V Lemay, TM Barrett, NL Crookston, et al. (2009). The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases. Scandinavian Journal of Forest Research 24(3): Goward, S, J Masek, W Cohen, G Moisen, G Collatz, et al. (2008). Forest disturbance and North American carbon flux. Eos 89(11). Grossmann, E, J Ohmann, M Gregory and H May (2009). Nationwide forest imputation study (NaFIS) - Western Team final report. McRoberts, RE, MD Nelson and DG Wendt (2002). Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest Neighbors technique. Remote Sensing of Environment 82(2 3): Moeur, M and AR Stage (1995). Most Similar Neighbor: An improved sampling inference procedure for natural resource planning. Forest Science 41(2): Pierce, KB Jr,JL Ohmann, MC Wimberly,, MJ Gregory, JS Fried. (2009). Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Can. J. For. Res. 39: Ohmann, JL and MJ Gregory (2002). Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research 32(4): Ohmann, JL, MJ Gregory, EB Henderson and HM Roberts (2011). Mapping gradients of community composition with nearestneighbour imputation: extending plot data for landscape analysis. Journal of Vegetation Science 22(4): Tomppo, E. (1991) Satellite image-based national forest inventory of Finland. In Proceedings of the Symposium on Global and Environmental Monitoring, Techniques and Impacts, Sept. 1990, Victoria, B.C. Int. Arch. Photogramm. Remote Sens. 28. pp Wilson, BT, AJ Lister and RI Riemann (2012). A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management 271(0):